Evidence map›Paper›PMID 25519314›Full record

ArticleBMC proceedings2014

Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees.

Laura Almasy, Thomas D Dyer, Juan M Peralta, Goo Jun, Andrew R Wood, Christian Fuchsberger, Marcio A Almeida, Jack W Kent, Sharon Fowler, Tom W Blackwell and 8 more

Open access · goldAbstract read
In one paragraph

Article in BMC proceedings, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
14.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 70 citations in OpenAlex.

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  14. Model-free Estimation of Recent Genetic Relatedness.American journal of human genetics · 2016
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 4 institutions in 2 countries.

Laura AlmasyDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
Thomas D DyerDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
Juan M PeraltaDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
Goo JunDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, USA.
Andrew R WoodGenetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK.
Christian FuchsbergerDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, USA.
Marcio A AlmeidaDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
Jack W KentDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
Sharon FowlerDivision of Clinical Epidemiology, Department of Medicine, University of San Antonio Health Science Center at San Antonio, San Antonio, Texas, 78229, USA.
Tom W BlackwellDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, USA.
Sobha PuppalaDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
Satish KumarDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
Joanne E CurranDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
Donna LehmanDivision of Clinical Epidemiology, Department of Medicine, University of San Antonio Health Science Center at San Antonio, San Antonio, Texas, 78229, USA.
Goncalo AbecasisDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, USA.
Ravindranath DuggiralaDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
John BlangeroDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
T2D-GENES Consortium
Texas Biomedical Research Institute · USStatistical Research (United States) · USThe University of Texas Health Science Center at San Antonio · USPeninsula College of Medicine and Dentistry · GB

Funding

GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
NIGMS NIH HHS R01 GM031575
6 · The paper itself

Abstract

Genetic Analysis Workshop 18 (GAW18) focused on identification of genes and functional variants that influence complex phenotypes in human sequence data. Data for the workshop were donated by the T2D-GENES Consortium and included whole genome sequences for odd-numbered autosomes in 464 key individuals selected from 20 Mexican American families, a dense set of single-nucleotide polymorphisms in 959 individuals in these families, and longitudinal data on systolic and diastolic blood pressure measured at 1-4 examinations over a period of 20 years. Simulated phenotypes were generated based on the real sequence data and pedigree structures. In the design of the simulation model, gene expression measures from the San Antonio Family Heart Study (not distributed as part of the GAW18 data) were used to identify genes whose mRNA levels were correlated with blood pressure. Observed variants within these genes were designated as functional in the GAW18 simulation if they were nonsynonymous and predicted to have deleterious effects on protein function or if they were noncoding and associated with mRNA levels. Two simulated longitudinal phenotypes were modeled to have the same trait distributions as the real systolic and diastolic blood pressure data, with effects of age, sex, and medication use, including a genotype-medication interaction. For each phenotype, more than 1000 sequence variants in more than 200 genes present on the odd-numbered autosomes individually explained less than 0.01-2.78% of phenotypic variance. Cumulatively, variants in the most influential gene explained 7.79% of trait variance. An additional simulated phenotype, Q1, was designed to be correlated among family members but to not be associated with any sequence variants. Two hundred replicates of the phenotypes were simulated, with each including data for 849 individuals.

Identifiers

PMID25519314
PMCPMC4145406
OpenAlexW1998004344

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.